Multi-objective programming for energy system based on the decomposition of carbon emission driving forces: A case study of Guangdong, China

Energy-related carbon emissions are increasing the rate of climate change, and controlling carbon emissions is a common challenge for the international public. Despite attempts to restrict the utilization of fossil energy and advancing technology for cleaner production, there has been little discussion on the determinants of change in carbon emissions for future scenarios and planning energy systems according to the analysis of low carbon development. In this study, a comprehensive energy optimization planning framework under a low-carbon mode is established. A framework based on the gray model (GM) and logarithmic mean Divisia index (LMDI) method are constructed to predict the emission mitigation potential and decompose the carbon emission driving factors. The decomposition results are key input prerequisites for the following energy optimization model: An interval parameter multiple-objective programming (IPMOP) optimization model, which is developed to support regional energy system administration by seeking the trade-offs among economic development, energy utilization, and environmental protection under multiple uncertainties. Furthermore, the proposed approach is applied to a case study in Guangdong, China. The results reveal that (a) the clean production effect (GDP per unit of atmospheric pollutants emission) would become the primary positive force for carbon emission increase, and the pollutant reduction effect (total atmospheric pollutants emission) would play the primary negative role; (b) the coal-dominated energy structure in Guangdong is expected to be transformed to a petroleum-dominated energy structure; (c) the GDP in Guangdong would steadily increase over time, but the pace of economic growth will decelerate, and the annual average growth rate of GDP for the coming fifteen years will be [3.67%, 4.26%]. This study provides a new pathway for policymakers to identify the determinants of carbon emission increase and to generate optimal solutions on a regional scale. •Developing a comprehensive energy optimization framework under a low carbon path.•Revealing the emission driving factors of future scenarios by integrating the LMDI and gray forecasting model.•Identifying the main driving and restraining forces of carbon emissions.•Generating optimal solutions to address the trade-offs among multiple objectives.•Providing decision support for energy systems in Guangdong Province, China.